Smoking Cessation Programs for Women in Non-reproductive Contexts: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Women's smoking and cessation behaviors are influenced by various sex- and gender- (SaG) related factors; however, most smoking cessation programs that do not target pregnant women follow a gender-neutral approach. We aimed to systematically review the literature on smoking cessation programs for women outside reproductive contexts to assess their effectiveness and how they address SaG-related barriers. METHODS: We selected experimental studies published between June 1, 2009, and June 7, 2023, that describe smoking cessation interventions designed exclusively for women. Two independent reviewers extracted study characteristics, intervention effectiveness, strategies to address SaG-related factors, and the studies' approach to gender equity using the gender integration continuum. We searched multiple databases to comprehensively identify relevant studies for inclusion. The protocol was registered with PROSPERO #CRD42023429054. RESULTS: Twenty-five studies were selected and summarized using a narrative synthesis. Of these, nine (36%) found a greater reduction in smoking in the intervention group relative to the comparison group. Nine studies addressed women's concerns about post-cessation weight gain; however, in only one of these did the intervention group show a greater likelihood of quitting smoking relative to the comparison group. In contrast, three of four studies tailored for women facing socioeconomic disadvantage, and three of four studies designed for women with medical comorbidities, reported a greater reduction in smoking behaviors in the intervention relative to the comparison group. Ten studies relied solely on counseling and did not provide participants with smoking cessation pharmacotherapy. Overall, studies addressed individual and community-level barriers to quitting, including post-cessation weight gain, lack of social support, psychological distress, and cultural influences. All but one study avoided using harmful gender norms to promote cessation. CONCLUSIONS: Strategies that address SaG-related barriers to quitting may improve cessation outcomes among women, particularly when tailored to meet the unique needs of specific groups such as those facing socioeconomic disadvantage. Future studies should combine best practices in smoking cessation treatment-behavioral counseling and pharmacotherapy-with new knowledge on how SaG factors influence motives for smoking and barriers to quitting. Such an approach could lead to more effective and equitable smoking cessation interventions for women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".